Methods, systems, and computer program products for generating data quality indicators for relationships in a database

ABSTRACT

The disclosed methods, systems, and computer-program products allow a business to generate data quality indicators for relationships in a database. In an embodiment, one or more relationships linked to a customer are retrieved from a database to form a set of relationships. A match confidence code is generated for each relationship based on a score generated by the comparison of customer data associated with the respective relationship and corresponding customer data obtained from an external industry database. A link confidence code is subsequently determined for the customer based on a score generated by the scores used to define the match confidence code for each relationship in the set of relationships and on internal data associated with each relationship in the set of relationships. The link confidence code for the customer and the match confidence codes and the respective scores for the set of relationships may be provided to an end user of the database in order to improve decisions made by the end user at the customer level.

BACKGROUND

1. Field of the Invention

This invention generally relates to managing information in a database,and in particular, it relates to methods for generating indicators ofdata quality in databases.

2. Related Art

Customers often have more than one account established through abusiness, especially with a service-oriented business such as afinancial services company or an insurance business. In the case of thefinancial services industry, for example, a single customer may have anycombination of a personal bank account, a mortgage, a line of credit(such as a home equity line of credit), a personal credit card, abusiness credit card, a rewards account, and one or more investmentaccounts with a single financial institution. In the insurance business,a single customer may have any combination of health insurance, autoinsurance, home owners insurance, and other kinds of insuranceprotection as well. Even with non-service businesses, a single customermay have multiple accounts. For example, a single customer may have oneaccount with a computer supply company for home purchases and anotheraccount with the same company for small business purchases.

It is important for a company to recognize that all of the customer'saccounts belong to a single customer and to link those accounts, inorder to appropriately market to the customer without overloading thecustomer. Further, ensuring that all accounts for a given customer are,in fact, accurately associated with that customer is vital forbusinesses that offer risk management and decision-support to theircustomers. The correct linking of accounts with a customer can improvethe accuracy of the financial company's estimate of the financial statusof the customer.

In practice, accurately linking accounts with a single customer provesto be a non-trivial undertaking. It is possible to associate one or moreaccounts with a single customer based on unique customer identifyinginformation, such as the customer name, social security number, date ofbirth, address, and other distinctive or unique identifiers. However,the association process is fallible, as it is possible that variationsmay creep into the way a customer's name or address is recorded, orsimply that errors are made during the process of collecting customeridentifying data. People change addresses over time, or change theirname, which can thwart efforts to make account associations based on thename, address, or other time-variant identification data.

Still another factor which makes it difficult to effectively recognizewhich accounts are, in fact, associated with a single customer is thesize of many businesses. A large service business, such as a largefinancial institution, may have multiple business units. Often, thesebusiness units do not efficiently or effectively share information,since in some cases data processing may be distributed over multiplecomputer systems and software systems. As a result customer informationcan be fragmented over these multiple data processing systems and theirassociated databases.

The difficulties inherent to the linking process often result in errorsin the linking process. For example, it is possible that that accountswhich actually belong to two separate customers may become associated,within the business database, with a single customer and thus beincorrectly linked. Further, it may also be possible that two accountsbelonging to a single customer may not be associated with that customerin the business database.

Therefore, it is essential to businesses, such as the financial servicesindustries, to recognize the quality of data associated with linkedaccounts and to address the underlying factors that contribute to thisdata quality. However, existing techniques are generally capable ofindicating only whether an account is linked to another account orlinked to a customer. These techniques are generally unable to provideany detailed accounting of the quality of the data associated with theaccounts or the quality of linking between these accounts.

SUMMARY OF THE INVENTION

Accordingly, the present disclosure introduces methods, systems, andcomputer-program products for generating data quality indicators forrelationships in a database.

According to various embodiments of the disclosed processes, one or morerelationships linked to a customer are retrieved from a database to forma set of relationships. A match confidence code is generated for eachrelationship in the set of relationships by comparing customer dataassociated with the respective relationship to corresponding customerdata obtained from an industry database. A link confidence code issubsequently determined for the customer based on internal dataassociated with the relationship and the generated match confidence codefor each relationship in the set of relationships. At least one of thelink confidence codes associated with the customer and the matchconfidence code associated with each relationship linked to the customerare provided to an end user of the relationship.

Further features and advantages of the present invention as well as thestructure and operation of various embodiments of the present inventionare described in detail below with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention will become more apparent from thedetailed description set forth below when taken in conjunction with thedrawings. The left-most digit of a reference number identifies thedrawing in which the reference number first appears.

FIG. 1 illustrates an exemplary method for estimating the accuracy ofrelationships linked to customers in a database.

FIG. 2 illustrates a method for generating match confidence codes thatmay be incorporated into the exemplary method of FIG. 1.

FIGS. 3 and 4 are examples that illustrate the method of FIG. 2.

FIG. 5 illustrates a method for generating link confidence codes thatmay be incorporated into the exemplary method of FIG. 1.

FIG. 6 is a diagram of an exemplary computer system upon which thepresent invention may be implemented.

DETAILED DESCRIPTION

The present invention, as described below, may be implemented in manydifferent embodiments of software, hardware, firmware, and/or theentities illustrated in the figures. Any actual software code with thespecialized control of hardware to implement the present invention isnot limiting of the present invention. Thus, the operational behavior ofthe present invention will be described with the understanding thatmodifications and variations of the embodiments are possible, given thelevel of detail presented herein.

I. Terminology

The terms “user”, “end user”, “consumer”, “customer”, “participant”,and/or the plural form of these terms are used interchangeablythroughout herein to refer to those persons or entities capable ofaccessing, using, being affected by and/or benefiting from the tooldescribed herein.

Furthermore, the terms “business” or “merchant” may be usedinterchangeably with each other and shall mean any person, entity,distributor system, software and/or hardware that is a provider, brokerand/or any other entity in the distribution chain of goods or services.For example, a merchant may be a grocery store, a retail store, a travelagency, a service provider, an on-line merchant or the like.

A “customer relationship,” as used herein, refers to a relationshipestablished between a customer and a business. In one example, acustomer relationship may be an account established between a customerand a service oriented business, such as a financial services company ora consumer travel agency. In the case of the financial servicesindustry, for example, a single customer may have any combination of apersonal bank account, a mortgage, a line of credit (such as a homeequity line of credit), a personal credit card, a business credit card,a rewards account, and one or more investment accounts with a singlefinancial institution.

A “transaction account” as used herein refers to an account associatedwith an open account or a closed account system (as described below).The transaction account may exist in a physical or non-physicalembodiment. For example, a transaction account may be distributed innon-physical embodiments such as an account number, frequent-flyeraccount, telephone calling account or the like. Furthermore, a physicalembodiment of a transaction account may be distributed as a financialinstrument. The terms “account provider” or “financial institution” asused herein refer to the financial institution associated with theaccount.

A financial transaction instrument may be traditional plastictransaction cards, titanium-containing, or other metal-containing,transaction cards, clear and/or translucent transaction cards, foldableor otherwise unconventionally-sized transaction cards, radio-frequencyenabled transaction cards, or other types of transaction cards, such ascredit, charge, debit, pre-paid or stored-value cards, or any other likefinancial transaction instrument. A financial transaction instrument mayalso have electronic functionality provided by a network of electroniccircuitry that is printed or otherwise incorporated onto or within thetransaction instrument (and typically referred to as a “smart card”), orbe a fob having a transponder and an RFID reader.

“Open cards” are financial transaction cards that are generally acceptedat different merchants. Examples of open cards include the AmericanExpress®, Visa®, MasterCard® and Discover® cards, which may be used atmany different retailers and other businesses. In contrast, “closedcards” are financial transaction cards that may be restricted to use ina particular store, a particular chain of stores or a collection ofaffiliated stores. One example of a closed card is a pre-paid gift cardthat may only be purchased at, and only be accepted at, a clothingretailer, such as The Gap® store.

Stored value cards are forms of transaction instruments associated withtransaction accounts, wherein the stored value cards provide cashequivalent value that may be used within an existing payment/transactioninfrastructure. Stored value cards are frequently referred to as gift,pre-paid or cash cards, in that money is deposited in the accountassociated with the card before use of the card is allowed. For example,if a customer deposits ten dollars of value into the account associatedwith the stored value card, the card may only be used for paymentstogether totaling no more than ten dollars.

With regard to use of a transaction account, users may communicate withmerchants in person (e.g., at the box office), telephonically, orelectronically (e.g., from a user computer via the Internet). During theinteraction, the merchant may offer goods and/or services to the user.The merchant may also offer the user the option of paying for the goodsand/or services using any number of available transaction accounts.Furthermore, the transaction accounts may be used by the merchant as aform of identification of the user. The merchant may have a computingunit implemented in the form of a computer-server, although otherimplementations are possible.

In general, transaction accounts may be used for transactions betweenthe user and merchant through any suitable communication means, such as,for example, a telephone network, intranet, the global, public Internet,a point of interaction device (e.g., a point of sale (POS) device,personal digital assistant (PDA), mobile telephone, kiosk, etc.), onlinecommunications, off-line communications, wireless communications, and/orthe like.

Persons skilled in the relevant arts will understand the breadth of theterms used herein and that the exemplary descriptions provided are notintended to be limiting of the generally understood meanings attributedto the foregoing terms.

II. Overview

The processes now introduced allow a business, such as aservice-oriented business or a financial services company, to generatedata quality indicators for relationships linked to customers and toimprove the quality of customer-level decision making based on theseindicators. In embodiments of such processes, one or more relationshipslinked to a customer are retrieved from a database to form a set ofrelationships. A match confidence code is generated for eachrelationship in the set of relationships based on a comparison ofcustomer data associated with the respective relationship andcorresponding customer data obtained from an external industry database.A link confidence code is subsequently determined for the customer basedon the generated match confidence code for each relationship in the setof relationships and internal data associated with each relationship inthe set of relationships. At least one of the link confidence code forthe customer and the match confidence codes for the set of relationshipslinked to the customer are subsequently provided to an end user of thedatabase.

III. Methods, Systems, and Computer-Program Products for EstimatingAccuracy of Linking of Customer Relationships

FIG. 1 is an overview of an exemplary method 100 for generating dataquality indicators for relationships linked to customers in a database.In one embodiment, the database may be associated with a business, andthe database may include a plurality of customer relationshipsestablished between the business and a corresponding plurality ofcustomers. For example, the customer relationship may be associated witha financial transaction instrument held by a customer and provided by afinancial services company, such as American Express Company, Inc., ofNew York, N.Y. In addition, the customer relationship may describe arelationship between a customer and a service-oriented company, such asa consumer travel agency or insurance company.

One or more relationships associated with a particular customer areretrieved from a database in step 102 to form a set of relationshipslinked to the customer. As described above, these relationships mayrepresent financial transaction instruments held by the customer, oralternatively, these customer relationships may describe relationshipsbetween the customer and a service-oriented company. In one embodiment,each retrieved relationship includes a single data record that describeselements of customer data associated with the relationship. Such dataelements may include, but are not limited to, a reference numberassociated with the retrieved relationship, as well as a name, address,date of birth, and social security number of the customer associatedwith the retrieved relationship.

In step 104, information currently associated with the customer isobtained from an external industry database. In one embodiment, theexternal industry database may be provided by a marketing data provider,such as Experian of Costa Mesa, Calif. The information may include anumber of discrete data elements, including, but not limited to, a nameof the customer, an address of the customer, a date of birth of thecustomer, and a social security number of the customer. In addition, anauthentication code associated with the customer may be obtained fromthe external industry database. In one embodiment, the authenticationcode indicates whether the customer is present in any of a number ofpublic records, including, but not limited to, genealogical records,databases of criminal offenders, and databases of professionallicensees.

In step 106, customer data associated with the relationship andcorresponding customer data obtained from the external industry databaseare processed to generate a match confidence code for each relationshipin the set of relationships. The match confidence code indicates thequality of the match between the customer data associated with therelationship and the customer data obtained from the external industrydatabase.

In one embodiment, the match confidence code for a relationship in theset of relationships is based on a comparison of customer dataassociated with the relationship and corresponding customer dataobtained from the external data provider. For example, a name, anaddress, a date of birth, and a social security number of a customerlinked to the respective relationship may be compared with acorresponding customer name, address, date of birth, and social securitynumber obtained from the external industry database. The comparison mayresult in a set of match scores for the relationship that individuallyand collectively indicate the quality of the match between therespective elements of customer data.

In additional embodiments, the authentication code associated with thecustomer, and obtained from the external industry database, may beappended to the set of match scores for the relationship. In such acase, the match confidence code generated for each relationship maydescribe both the authentication of the customer and the quality of thematch between customer data associated with the relationship andcustomer data obtained from the external industry database.

In step 106, the match confidence code for each relationship in the setof relationships may be selected from one of a plurality of confidencelevels depending on the authentication code obtained from the externalindustry database and the set of match scores for each relationship inthe set of relationships. The plurality of confidence levels mayinclude, but are not limited to, confidence levels of LOW, MAYBE, WEAKYES, STRONG YES, and EXACT. Further, in an additional embodiment, ahigher confidence level may be assigned to a relationship linked to anauthenticated customer than would be assigned to a comparablerelationship linked to an unauthenticated customer. One skilled in theart would recognize that additional confidence levels, includingnumerical values, might be assigned to relationships without departingfrom the spirit and scope of the present invention.

The match confidence codes for the relationships in the set ofrelationships linked with the customer are then processed in step 108 togenerate a link confidence code for the customer. In contrast to thematch confidence code, the link confidence code is generated at thecustomer level, and the link confidence code indicates the quality ofthe linking between the customer and the set of relationships. As such,the link confidence code may serve as a metric for identifying thosecustomers that are linked incorrectly to one or more relationships.

In step 108, the link confidence code of the customer may be determinedfrom the match confidence code generated for each relationship in theset of relationships and the internal information associated with eachrelationship in the set of relationships. In one embodiment, theinternal information may include data that indicates an age of thecustomer information associated with the relationship, including, butnot limited to, the time period since the customer information wasupdated (e.g., thirty days, one hundred days, one year, etc.). Inadditional embodiments, the internal information may indicate an age ofcustomer data entered onto a customer service website associated withthe relationship (e.g., a customer service website for a financialtransaction instrument). One skilled in the art would recognize thatadditional sources and varieties of internal information may be used togenerate the link confidence score for the customer without departingfrom the spirit and scope of the present invention.

The link confidence code for the customer may be selected in step 108from one of a plurality of confidence levels depending on the matchconfidence codes for the set of relationships and the internal data. Theplurality of confidence levels may include, but are not limited to,confidence levels of LOW, MAYBE, WEAK YES, STRONG YES, and EXACT.Further, in additional embodiments, the internal information associatedwith the customer may be weighted such that a higher confidence levelmay be generated for a relationship having more recently updatedcustomer information than would be generated for a relationship havingless frequently updated customer information. One skilled in the artwould recognize that additional confidence levels, including numericalvalues, might be assigned to relationships without departing from thespirit and scope of the present invention.

In step 110, at least one of the link confidence codes for the customerand the match confidence code for each relationship in the set ofrelationships linked to the customer is provided to an end user of thedatabase. In one embodiment, the end user of the database analyzes thelink confidence code and match confidence codes in an effort to improvethe quality of decisions made at the customer level.

For example, a business, such as a financial services company, may usethe link confidence code to make decisions on customer exposure andrisk. In such an embodiment, the financial services company may bewilling to assume little additional risk or exposure for a customerassigned a low-level link confidence code (e.g., LOW or MAYBE), therebycausing potential point-of-sale (POS) disruptions for the customer. Bysegmenting certain classes of customers of the financial servicescompany (such as high value (HV) customers), the financial servicescompany could target its efforts to increase the match and linkconfidence codes of segments of customers in an effort to reduce POSdisruption and improve brand experience.

Further, in additional embodiments, the link confidence code for thecustomer and the match confidence code for each relationship in the setof the relationships may be analyzed in an effort to improve the qualityof data and linkages in the database. Additionally, the link and matchconfidence codes may indicate actionable items that improve the datacollection and data input processes for relationships in the database.

FIG. 2 illustrates a method 200 for generating match confidence codesthat may be incorporated into step 106 of exemplary method 100 ofFIG. 1. In step 202, a relationship is selected from a set ofrelationships linked to the customer. Subsequently, in step 204, each ofa plurality of customer data elements associated with the selectedrelationship is matched to a corresponding plurality of customer dataelements obtained from an external industry database to generate anelemental match score for each data element associated with therelationship. In one embodiment, step 204 may compare a name, anaddress, a date of birth, and a social security number associated withthe selected relationship to corresponding customer data obtained fromthe external data provider.

The elemental match score assigned in step 204 may quantify thecloseness of a match between the respective data elements. For example,if a name associated with the selected relationship is an exact match tothat obtained from the data provider, an elemental score of five (5) maybe assigned to the name element. If, however, a complete mismatch existsbetween the name associated with the relationship and that obtained fromthe data provider, then the name element may be assigned an elementalmatch score of one (1). Elemental match scores between one (1) and five(5) may be assigned based on a quality of the match between dataassociated with the relationship and either a current or a historicalvalue of corresponding data obtained from the data provider. Althoughdescribed in terms of the name data element, the process outlined abovemay be employed to assign elemental match scores to each data elementassociated with the relationship.

The individual elemental match scores for the selected relationship areassembled in step 206 into a set of match scores for the selectedrelationship, and an authentication code obtained from the externalindustry database is appended to set of match scores in step 208 to forma match pattern for the selected relationship. For example, if elementalmatch scores are generated for a name, address, date of birth, andsocial security number associated with the relationship, then thecorresponding set of match scores would contain four elements and thematch pattern would contain five data elements (e.g., the set of matchscores and the authentication code).

In step 210, a rule is applied to the set of match scores for therespective relationship and the authentication code to generate a matchconfidence code for the respective relationship. As described above, thematch confidence code is a metric that describes the quality of the dataassociated with the relationship. In one embodiment, the matchconfidence code for the respective relationship may be selected from oneof a plurality of confidence levels based on the authentication code andon the quality of customer data associated with the relationship. Forexample, match confidence codes of LOW, MAYBE, WEAK YES, STRONG YES, andEXACT may be generated for the selected relationship. Additionally, instep 210, a boosting algorithm may be applied to the set of match scoresfor the respective relationship to generate a composite score, and oneof the plurality of confidence levels may be selected for the respectiverelationship based on the composite score. Further, in an additionalembodiment, a higher confidence level may be generated for arelationship linked to an authenticated customer than would be generatedfor a comparable relationship linked to an unauthenticated customer. Oneskilled in the art would recognize that any number of additionalconfidence levels, including numerical values, might be assigned withoutdeparting from the spirit and scope of the present invention.

Step 212 then processes the set of relationships to determine whether amatch confidence code has been generated for each relationship in theset of relationships. If each relationship within the set ofrelationships has been assigned a match confidence code, then step 214passes the set of match confidence codes for the set of relationshipsback to the exemplary method 100 of FIG. 1 for additional processing.If, however, step 212 identifies that relationships in the set ofrelationships lack match confidence codes, then the method passes backto step 202, and an additional relationship is selected for processing.

FIG. 3 is an example that illustrates the method of FIG. 2. In FIG. 3,elemental match scores for a name, an address (“Addr.”), a socialsecurity number (“SSN”), and a date of birth (“YOB”) associated with arelationship have been assembled into a set of match scores for therelationship. Further, an authentication code (“Auth. Code”) has beenobtained from the external industry database, and the authenticationcode has been appended to the set of match scores to form a matchpattern for the relationship.

As described above, a match pattern includes five elements ofinformation that describe the quality of the match between dataassociated with the relationship and customer data obtained from theexternal industry database. In the example of FIG. 3, each individualelemental score (e.g., that for name, address, social security number,and date of birth) ranges from zero (0) to five (5), depending upon thequality of the match between the relationship data and the correspondingcustomer data obtained from the external industry database.

In one embodiment, a match between relationship data and currentcustomer data obtained from the external industry database is assigned ahigher elemental match score than a corresponding match betweenrelationship data and historical customer data obtained from theexternal industry database. For example, an elemental match score offive (5) for a data element indicates that the data element associatedwith the relationship exactly matches a current value of the dataelement obtained from the external industry database. In contrast, anexact match between the data element associated with the relationshipand a corresponding, historical element of customer data obtained fromthe external industry database would result an elemental match score ofthree (3). Similarly, for example, an elemental match score of (4)indicates that the relationship data element is similar to a currentvalue of the customer data element obtained from the external industrydatabase, while an elemental match score of one (1) indicates that therelationship data element is similar to a historical value of thecorresponding customer data element obtained from the external industrydatabase. A score of zero (0) would be assigned only in those caseswhere the data element is missing from the relationship data and/or thecustomer data obtained from the external industry database.

In the example of FIG. 3, the generated match pattern for therelationship has a value of “N5201,” thus indicating that the customerassociated with the relationship has not been authenticated. Further,the match pattern indicates that the name associated with therelationship is an exact match with the current customer name obtainedfrom the external industry database. The match pattern also indicatesthat the address associated with the relationship is similar to aprevious (or historical) address obtained from the external industrydatabase, and the date of birth associated with the relationship and thedate of birth obtained from the external industry database are completemismatches. In addition, the match pattern indicates that the socialsecurity number is missing from either the customer data associated withthe relationship or the customer data obtained from the externalindustry database.

In example of FIG. 3, the elemental match scores have been described interms of a particular range of scores based on relationship data,current customer data, and historical customer data. One skilled in theart would recognize that elemental match scores based on a variety ofdifferent parameters and characterized by a variety of ranges may beassigned to data elements without departing from the spirit and scope ofthe present invention. Further, one skilled on the art would alsorecognize that the match pattern described in FIG. 3 could beconstructed from any number of elemental match scores without departingfrom the spirit and scope of the present invention

FIG. 4 is second example that illustrates the method of FIG. 2. FIG. 4illustrates the application of a rule to generate a match confidencecode for a relationship based on a set of match scores for therelationship and an authentication code (e.g., the match pattern for therelationship). For example, a relationship may be assigned a matchconfidence code of LOW if the match pattern for that relationshipincludes any combination of missing and mismatched data elements. Insuch a case, there is little probability that the data associated withthe account is correct. Examples of match patterns that would beassigned a LOW confidence level include, but are not limited to, N5400,Y1301, and Y1105.

Further, a relationship may be assigned a confidence level of WEAK YESif the match pattern for that relationship includes two or more strongdata element matches and if the customer associated with therelationship has been authenticated. In such a case, the relationship islikely to be linked correctly to the customer. Examples of matchpatterns that would be assigned a WEAK YES confidence level includeY4505, Y3350, and Y5504.

However, a match pattern associated with a relationship may not providea clear indication of the quality of the match. For example, the matchpattern may include strong matches between data associated with therelationship and obtained from an external industry database, whileindicating that the customer cannot be authenticated. Alternatively, thematch pattern may indicate one a single, strong element level match fora relationship linked to an authenticated customer. In such instances,the relationship may be assigned a confidence level of MAYBE, thusimplying that a match can neither be confirmed nor rejected with anydegree of confidence. Examples of match patterns that would be assigneda MAYBE confidence level include Y5300, N5150, and Y5400.

A relationship may be assigned a match confidence code of STRONG YES ifthe match pattern for the relationship indicates three out of four dataelements are strong matches with data associated with an authenticatedcustomer. In such an instance, there is a very strong likelihood thatthe relationship data matches data matches that obtained from theexternal industry database, and further, that the relationship iscorrectly linked to the customer. Examples of match patterns that wouldbe assigned a STRONG YES confidence level include Y4555, Y5354, andY5554.

If a match pattern for a relationship indicates that all keyrelationship data elements match corresponding data elements obtainedfor the customer, then the relationship is assigned a confidence levelof EXACT. In the example of FIG. 5, the key data elements include thename, address, and social security number of the customer. As such,there is no doubt that the relationship is correctly linked to theauthenticated customer. Examples of match patterns that would beassigned an EXACT confidence level include Y5555 and Y5550.

In the example of FIG. 4, match patterns are characterized by fiveconfidence levels based on the quality of the data associated withrelationship and the authentication of the customer. One skilled in theart would recognize that the various confidence levels might be assignedaccording to a variety of parameters without departing from the spiritand scope of the present invention.

FIG. 5 illustrates a method 500 for generating link confidence codesthat may be incorporated into step 108 of exemplary method 100 ofFIG. 1. As described above with reference to FIG. 1, the link confidencecode is generated at the customer level, and the link confidence codeindicates the quality of linking between customers and relationships ina database.

In step 502, match confidence codes are obtained for each relationshipin a set of relationships linked to a customer. Additionally, in step504, internal information associated with each relationship in the setof relationships is appended to the corresponding match confidence codefor each relationship in the set of relationships.

In one embodiment, the internal information may include an age of thecustomer information associated with the relationship, including, butnot limited to, the time period since the customer information wasupdated (e.g., thirty days, one hundred days, one year, etc.). Inadditional embodiments, the internal information may indicate an age ofcustomer information entered onto a customer service website associatedwith the relationship (e.g., a customer service website for a financialtransaction instrument). One skilled in the art would recognize thatadditional sources and varieties of internal information may be used togenerate the link confidence score for the customer without departingfrom the spirit and scope of the present invention.

In step 506, a rule is applied to the internal information and matchconfidence code associated with each relationship in the set ofrelationships to generate the link confidence code for the customer.Further, in step 506, the applied rule generates a numerical linkingscore indicative of the quality of customer linking, and the confidencelevel is selected on the basis of this score. For example, in oneembodiment, a weighted average may be applied to the set of match scoresand the internal information associated with each relationship in theset of relationships to generate the linking score. In one embodiment,the link confidence code may be selected from a plurality of confidencelevels based on the internal data and match confidence code associatedwith each relationship in the set of relationships.

The plurality of confidence levels may include, but are not limited to,confidence levels of LOW, MAYBE, WEAK YES, STRONG YES, and EXACT.Further, in an additional embodiment, the internal informationassociated with the customer may be weighted such that a higherconfidence level may be selected for a relationship having morefrequently updated customer information than would be selected for arelationship having less frequently updated customer information. Oneskilled in the art would recognize that additional confidence levels,including numerical values, might be assigned to relationships withoutdeparting from the spirit and scope of the present invention. Onceassigned in step 506, the link confidence code for the customer ispassed back to the exemplary method of FIG. 1 for additional processing.

IV. Exemplary Computer Systems

FIG. 6 is a diagram of an exemplary computer system 600 upon which thepresent invention may be implemented. The exemplary computer system 600includes one or more processors, such as processor 602. The processor602 is connected to a communication infrastructure 606, such as a bus ornetwork. Various software implementations are described in terms of thisexemplary computer system. After reading this description, it willbecome apparent to a person skilled in the relevant art how to implementthe invention using other computer systems and/or computerarchitectures.

Computer system 600 also includes a main memory 608, preferably randomaccess memory (RAM), and may include a secondary memory 610. Thesecondary memory 610 may include, for example, a hard disk drive 612and/or a removable storage drive 614, representing a magnetic tapedrive, an optical disk drive, etc. The removable storage drive 614 readsfrom and/or writes to a removable storage unit 618 in a well-knownmanner. Removable storage unit 618 represents a magnetic tape, opticaldisk, or other storage medium that is read by and written to byremovable storage drive 614. As will be appreciated, the removablestorage unit 618 can include a computer usable storage medium havingstored therein computer software and/or data.

In alternative implementations, secondary memory 610 may include othermeans for allowing computer programs or other instructions to be loadedinto computer system 600. Such means may include, for example, aremovable storage unit 622 and an interface 620. An example of suchmeans may include a removable memory chip (such as an EPROM, or PROM)and associated socket, or other removable storage units 622 andinterfaces 620, which allow software and data to be transferred from theremovable storage unit 622 to computer system 600.

Computer system 600 may also include one or more communicationsinterfaces, such as communications interface 624. Communicationsinterface 624 allows software and data to be transferred betweencomputer system 600 and external devices. Examples of communicationsinterface 624 may include a modem, a network interface (such as anEthernet card), a communications port, a PCMCIA slot and card, etc.Software and data transferred via communications interface 624 are inthe form of signals 628, which may be electronic, electromagnetic,optical or other signals capable of being received by communicationsinterface 624. These signals 628 are provided to communicationsinterface 624 via a communications path (i.e., channel) 626. Thischannel 626 carries signals 628 and may be implemented using wire orcable, fiber optics, an RF link and other communications channels. In anembodiment of the invention, signals 628 include data packets sent toprocessor 602. Information representing processed packets can also besent in the form of signals 628 from processor 602 throughcommunications path 626.

The terms “computer program medium” and “computer usable medium” areused to refer generally to media such as removable storage units 618 and622, a hard disk installed in hard disk drive 612, and signals 628,which provide software to the computer system 600.

Computer programs are stored in main memory 608 and/or secondary memory610. Computer programs may also be received via communications interface624. Such computer programs, when executed, enable the computer system600 to implement the present invention as discussed herein. Inparticular, the computer programs, when executed, enable the processor602 to implement the present invention. Where the invention isimplemented using software, the software may be stored in a computerprogram product and loaded into computer system 600 using removablestorage drive 618, hard drive 612 or communications interface 624.

V. Conclusion

The disclosed processes provide an accessible, actionable, andeasily-executed platform for generating data quality indicators forrelationships in a database. Through the disclosed processes,businesses, such as financial service companies, obtain valuable insighton customer linkages based on industry information, thus enablingbusinesses to improve customer linkages and to make better decisions onrisk, servicing, and marketing at the customer level. Further, merchantsbenefit from a reduction in (POS) disruptions due to linking errors,thus increasing the potential sales volume of merchants and increasingcustomer satisfaction. Customers also benefit from more accurate andtimely decisions on risk, marketing, and customer service.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant art(s) that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentinvention. Thus, the present invention should not be limited by any ofthe above described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

In addition, it should be understood that the figures illustrated in theattachments, which highlight the functionality and advantages of thepresent invention, are presented for example purposes only. Thearchitecture of the present invention is sufficiently flexible andconfigurable, such that it may be utilized (and navigated) in ways otherthan that shown in the accompanying figures.

Further, the purpose of the following Abstract is to enable the U.S.Patent and Trademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the technical disclosure ofthe application. The Abstract is not intended to be limiting as to thescope of the present invention in any way.

1. A method for generating data quality indicators for relationships,wherein relationships are established between a customer and a business,in a database, comprising: comparing, by a computer based system forgenerating the data quality indicators for the relationships, for eachrelationship in a set of relationships, data associated with therespective relationship and corresponding data obtained from an industrydatabase to generate a match confidence code for each relationship inthe set of relationships, wherein the set of relationships includesmultiple relationships obtained from a database; determining, by thecomputer based system, a link confidence code for the customer, whereinthe link confidence code comprises an indication of the quality of thelinking between the customer and the set of relationships based on thegenerated match confidence code for each relationship in the set ofrelationships and internal information associated with each relationshipin the set of relationships, wherein the link confidence code isdistinct from the match confidence code; and providing, by the computerbased system, at least one of the link confidence code for the customerand the match confidence code for each relationship in the set ofrelationships linked to the customer.
 2. The method of claim 1, furthercomprising verifying quality of relationships in the database using atleast one of the link confidence code for the customer and the matchconfidence code for each relationship.
 3. The method of claim 1, whereinthe comparing step comprises: matching, by the computer based system,each of a plurality of data elements associated with the respectiverelationship to corresponding data elements obtained from the industrydatabase to generate an elemental match score for each of the pluralityof data elements; assembling, by the computer based system, theelemental match scores for the respective relationship into a set ofmatch scores for the respective relationship; appending, by the computerbased system, an authentication code obtained from the industry databaseto the set of match scores for the respective; and applying a rule, bythe computer based system, to generate the match confidence code for therespective relationship based on the set of match scores for therespective relationship and the authentication code.
 4. The method ofclaim 3, wherein the matching step comprises matching, by the computerbased system, at least one of a name, an address, a social securitynumber, and a date of birth associated with the respective relationshipto corresponding data elements obtained from the industry database. 5.The method of claim 3, wherein the applying step comprises selecting, bythe computer based system, the match confidence code for the respectiverelationship from a plurality of confidence levels based on the matchpattern and the authentication code of the respective relationship. 6.The method of claim 1, wherein the determining step comprises:appending, by the computer based system, the internal data associatedwith each relationship in the set of relationships to the matchconfidence code for each relationship in the set of relationships; andapplying a rule, by the computer based system, to the internal data andmatch confidence code associated with each relationship the set ofrelationships to generate the link confidence code for the customer. 7.The method of claim 6, wherein the applying step comprises selecting, bythe computer based system, the link confidence code for the customerfrom a plurality of confidence levels based on the internal data andmatch confidence code associated with each relationship in the set ofrelationships.
 8. The method of claim 6, wherein the appending stepcomprises adding, by the computer based system, an age of the dataassociated with the relationship to the match confidence code for eachrelationship in the set of relationships.
 9. A system for generatingdata quality indicators for relationships, wherein relationships areestablished between a customer and a business, in a database,comprising: a processor for generating the data quality indicators forrelationships; and a memory in communication with the processor, thememory for storing a plurality of processing instructions for directingthe processor to: compare, for each relationship in a set ofrelationships, data associated with the respective relationship andcorresponding data obtained from an industry database to generate amatch confidence code for each relationship in the set of relationships,wherein the set of relationships includes multiple relationshipsobtained from a database; determine a link confidence code for thecustomer, wherein the link confidence code comprises an indication ofthe quality of the linking between the customer and the set ofrelationships based on the generated match confidence code for eachrelationship in the set of relationships and internal informationassociated with each relationship in the set of relationships, whereinthe link confidence code is distinct from the match confidence code; andprovide, at least one of the link confidence code for the customer andthe link confidence code for each relationship in the set ofrelationships linked to the customer.
 10. The system of claim 9, whereinthe instructions for directing the processor to compare compriseinstructions for directing the processor to: match each of a pluralityof data elements associated with the respective relationship tocorresponding data elements obtained from the industry database togenerate an elemental match score for each of the plurality of dataelements; assemble the elemental match scores for the respectiverelationship into a set of match scores the respective relationship;append an authentication code obtained from the industry database to theset of match scores for the respective relationship; and apply a rule togenerate the match confidence code for the respective relationship basedon the match scores for the respective relationship and theauthentication code.
 11. The system of claim 10, wherein theinstructions for directing the processor to match comprise instructionsfor directing the processor to: match at least one of a name, anaddress, a social security number, and a date of birth associated withthe respective relationship to corresponding data elements associatedwith the customer.
 12. The system of claim 10, wherein the instructionsfor directing the processor to apply comprise instructions for directingthe processor to: select the match confidence code for the respectiverelationship from a plurality of confidence levels based on the matchpattern and the authentication code of the respective relationship. 13.The system of claim 9, wherein the instructions for directing theprocessor to determine comprise instructions for directing the processorto: append the internal data associated with each relationship in theset of relationships to the match confidence code for each relationshipin the set of relationships; and apply a rule to the internal data andmatch confidence code associated with each relationship in the set ofrelationships to generate the link confidence code for the customer. 14.The system of claim 10, wherein the instructions for directing theprocessor to apply comprise instructions for directing the processor to:select the link confidence code for the customer from a plurality ofconfidence levels based on the internal data and match confidence codeassociated with each relationship in the set of relationships.
 15. Thesystem of claim 10, wherein the instructions for directing the processorto append comprise instructions for directing the processor to: appendan age of the data associated with the relationship to the matchconfidence code for each relationship in the set of relationships.
 16. Anon-transitory computer readable medium having stored thereonnon-transitory sequences of instruction, the sequences of instructionincluding instruction which, in response to execution by a computerbased system for generating data quality indicators for relationships,causes the computer based system to perform the operations to generatethe data quality indicators for relationships in a database, whereinrelationships are established between a customer and a business,comprising: comparing, by the computer based system, for eachrelationship in a set of relationships, data associated with therespective relationship and corresponding data obtained from an industrydatabase to generate a match confidence code for each relationship inthe set of relationships, wherein the set of relationships includesmultiple relationships obtained from a database; determining, by thecomputer based system, a link confidence code for the customer, whereinthe link confidence code comprises an indication of the quality of thelinking between the customer and the set of relationships based on thegenerated match confidence code for each relationship in the set ofrelationships and internal information associated with each relationshipin the set of relationships, wherein the link confidence code isdistinct from the match confidence code; and providing, by the computerbased system, at least one of the link confidence code for the customerand the link confidence code for each relationship in the set ofrelationships linked to the customer.
 17. The computer readable mediumof claim 16, wherein the comparing comprises: matching, by the computerbased system, each of a plurality of data elements associated with therespective relationship to corresponding data elements obtained from theindustry database to generate an elemental match score for each of theplurality of data elements; assembling, by the computer based system,the elemental match scores for the respective relationship into a set ofmatch scores the respective relationship; appending, by the computerbased system, an authentication code obtained from the industry databaseto the set of match scores for the respective relationship; andapplying, by the computer based system, a rule to generate the matchconfidence code for the respective relationship based on the matchscores for the respective relationship and the authentication code. 18.The computer readable medium of claim 17, wherein the applyingcomprises: selecting, by the computer based system, the match confidencecode for the respective relationship from a plurality of confidencelevels based on the match pattern and the authentication code of therespective relationship.
 19. The computer readable medium of claim 16,wherein the determining comprises: appending, by the computer basedsystem, the internal data associated with each relationship in the setof relationships to the match confidence code for each relationship inthe set of relationships; and applying, by the computer based system, arule to the internal data and match confidence code associated with eachrelationship in the set of relationships to generate the link confidencecode for the customer.
 20. The computer readable medium of claim 19,wherein the applying comprises: appending, by the computer based system,an age of the data associated with the relationship to the matchconfidence code for each relationship in the set of relationships.